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Sustainable Development Goal 12 (SDG 12) requires sustainable production and consumption. One indicator named in the SDG for resource use is the (national) material footprint. A method and disaggregated data basis that differentiates the material footprint for production and consumption according to, e.g., sectors, fields of consumption as well as socioeconomic criteria does not yet exist. We present two methods and its results for analyzing resource the consumption of private households based on microdata: (1) an indicator based on representative expenditure data in Germany and (2) an indicator based on survey data from a web tool. By these means, we aim to contribute to monitoring the Sustainable Development Goals, especially the sustainable management and efficient use of natural resources. Indicators based on microdata ensure that indicators can be disaggregated by socioeconomic characteristics like age, sex, income, or geographic location. Results from both methods show a right-skewed distribution of the Material Footprint in Germany and, for instance, an increasing Material Footprint with increasing household income. The methods enable researchers and policymakers to evaluate trends in resource use and to differentiate between lifestyles and along socioeconomic characteristics. This, in turn, would allow us to tailor sustainable consumption policies to household needs and restrictions.
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sustainability
Article
Measure or Management?—Resource Use Indicators
for Policymakers Based on Microdata by Households
Johannes Buhl 1, Christa Liedtke 1,2, Jens Teubler 1, *, Katrin Bienge 1and Nicholas Schmidt 3
1Wuppertal Institut fuer Klima, Umwelt, Energie gGmbH, Division Sustainable Production and
Consumption, Doeppersberg 19, 42103 Wuppertal, Germany; johannesbuhlsonthofen@gmail.com (J.B.);
christa.liedtke@wupperinst.org (C.L.); katrin.bienge@wupperinst.org (K.B.)
2Industrial Design, Folkwang University of the Arts, Klemensborn 39, 45239 Essen, Germany
3Faculty of Management and Economics, Ruhr University Bochum, Universitätsstraße 150,
44801 Bochum, Germany; nicholas.schmidt1995@gmail.com
*Correspondence: jens.teubler@wupperinst.org
Received: 28 September 2018; Accepted: 16 November 2018; Published: 28 November 2018

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Abstract:
Sustainable Development Goal 12 (SDG 12) requires sustainable production and consumption.
One indicator named in the SDG for resource use is the (national) material footprint. A method and
disaggregated data basis that differentiates the material footprint for production and consumption
according to, e.g., sectors, fields of consumption as well as socioeconomic criteria does not yet exist.
We present two methods and its results for analyzing resource the consumption of private households
based on microdata: (1) an indicator based on representative expenditure data in Germany and
(2) an indicator based on survey data from a web tool. By these means, we aim to contribute to
monitoring the Sustainable Development Goals, especially the sustainable management and efficient
use of natural resources. Indicators based on microdata ensure that indicators can be disaggregated
by socioeconomic characteristics like age, sex, income, or geographic location. Results from both
methods show a right-skewed distribution of the Material Footprint in Germany and, for instance,
an increasing Material Footprint with increasing household income. The methods enable researchers
and policymakers to evaluate trends in resource use and to differentiate between lifestyles and along
socioeconomic characteristics. This, in turn, would allow us to tailor sustainable consumption policies to
household needs and restrictions.
Keywords:
sustainable production and consumption; resource indicator; sustainable development
goals; material footprint; household consumption; microdata
1. Introduction
Meeting the resource demand of a growing global consumer class increasingly affects the
environment and places a burden on climate and ecosystems [
1
]. Since household consumption
and production for consumer goods are at the core of the present resource-intensive lifestyles, it is
important to analyze the behavior of private households and assist them in transforming their routines
into more sustainable ones. This means providing new technologies, products, and services that
enable, perhaps even stimulate, a resource-friendly life. Production and consumption in this sense form
an interlaced system that can only be thought and developed in an integrated way.
Resource efficiency in the context of sustainable production and consumption is currently gaining
attention on a national and international level. The current trend towards Product Service Systems (PSS)
as an approach for increasing sustainability can contribute to a sustainable way of linking consumption
and production [
2
5
]. Several attempts have been made to support the development of low-resource
and socially accepted approaches of integrating production and consumption. Examples for this
Sustainability 2018,10, 4467; doi:10.3390/su10124467 www.mdpi.com/journal/sustainability
Sustainability 2018,10, 4467 2 of 16
are the Consumer Information Program of the 10 Year Framework of Programmes on Sustainable
Consumption and Production (10YFP) as well as the European Union with its Ecodesign Directive [
6
,
7
].
The Sustainable Development Goal 12 (SDG 12) “Ensure sustainable production and consumption
patterns” integrates a wide range of stakeholders into the process of increasing sustainability in
consumption and production [
8
]. However, the ambitious SDGs and their subgoals require appropriate
indicators for measuring the status quo and the progress until 2030. There is a lack of indicators
which are able to provide the necessary differentiation for socioeconomic characteristics like sex, age,
or income [
9
] and fields of consumption like housing or mobility that hampers the process of providing
improved assistance for producers and consumers in implementing more sustainable product-service
systems and production and consumption patterns [10] as advocated by SDG 12.
Germany published its first sustainability strategy in 2002 and reports the progress towards its
goals every four years [
11
]. This strategy includes different indicators for measuring the development
of sustainability in Germany. The latest update from 2016 adopts the framework of the Sustainable
Development Goals (SDGs). Besides this strategy, Germany implemented a National Program on
Sustainable Consumption that aims to identify the relevant fields of action and adequate measures [
12
].
It gives five guiding principles for a sustainable consumption policy, for example enabling consumers to
implement a sustainable way of consumption. In this respect, the German Program on Resource Efficiency
(Progress II) demands the implementation of a National Program on Sustainable Consumption in order to
promote resource efficiency in consumption [
13
]. Therefore the program strives to develop and improve its
set of indicators for a better measurement of the effects of changes in consumption. Behavioral changes in
favor of more resource efficient consumption are still hampered by obstacles such as a lack of information
and personalized feedback applications. The National Program on Sustainable Consumption suggests
providing such information by the use of assisting carbon and resource calculators [12].
Certain routines and social practices in consumption, as well as patterns in production and existing
business models, complicate a change towards a more sustainable behavior [
14
]. Sustainable consumption
requires sustainably designed product service systems and infrastructures [
15
]. It is only possible to shape
both together and step by step. Progress or regression, as well as rebound effects, must be visible and
therefore demonstrable. Indicators play a crucial role in setting up goals and measuring progress in this
regard. They simplify the complex cause-effect chains within our societies, economies, and with our
environment. On a national level, indicators can be used to develop pathways for sustainability or to
identify trends. Scenario developers usually use these goals and indicators to define a target corridor
in comparison to a status quo or business-as-usual. They are but a tool for aggregated measurement of
impacts and not their management.
However, indicators can potentially also be a tool to evaluate and inform in a differentiated
way by depicting and sometimes explaining the differences—with the means of modern societies
almost in real time. This would make them relevant not only politically (programmes and measures),
but also in everyday decision-making situations, whether in a company (products, infrastructures)
or in a household (lifestyles). This can be achieved using microdata (e.g., from online surveys) and
combining it with already existing methods.
Recent research has managed to quantify some levels of sustainable resource use. While the
global material extraction has drastically increased over the last four decades (from 26.7 billion tonnes
in 1970 to 75.6 billion tonnes in 2010) [
16
], material consumption levels in Europe already reached
40 tonnes per capita and more at the beginning of the century [
17
]. By comparison, Lettenmeier et al.
calculated a sustainable level of only 8 tonnes of a Material Footprint (per person and year) [
18
],
using the MIPS concept (Material Input Per unit of Service) This means a reduction by the factor
five, which requires an appropriate consumer policy and education for sustainable consumption
patterns [
19
]. MIPS (developed by Schmidt-Bleek in the 1990s [
20
]) provides micro-economic indicators
for the resource use of households that include the extraction of materials with and without economic
use (e.g., overburden from mining). Its methodology is based on Material-Flow-Accounting and
compatible with similar input indicators such as cumulated energy demand (ced) or cumulated raw
Sustainability 2018,10, 4467 3 of 16
material demand (KRA). Its indicator Material Footprint can also be adapted to the currently suggested
SDG 12 indicator with the same name. Recent methodological developments make use of improved
LCA data [2125].
Further research in the field also allowed us to differentiate between different household types
(milieus) and their resource use, although limited to small samples of selected households using
a diary approach. It could also be shown that the calculation of Material Footprints for households
is compatible with methods for the calculation of Carbon Footprints, thus allowing us to compare
lifestyles with high resource use but low carbon intensity and vice versa [21,26].
Two tools have developed that aim at supporting consumers in transforming their consumption
patterns and are supposed to develop synergy effects by combining them. Buhl et al. [
10
] developed
a Material Footprint (MF) indicator based on the MIPS concept and microeconomic expenditure data
in Germany. This indicator was already used to analyze the behavior of households in Germany and
the German federal state of North Rhine-Westphalia (NRW) and allows for differentiation between
regions (here, the federal states of Germany and Germany itself), socioeconomic characteristics of
households and between categories of consumption. The other method is based on microdata that is
directly obtained from an online tool. The “Resource Calculator” [
27
] is a free online application that
enables consumers to examine their consumption patterns by calculating their own Material Footprint.
Consumers can also supply information about their socioeconomic characteristics on a voluntary
and anonymous basis (such as age or years of schooling). Thus, the Resource Calculator provides
an anonymized dataset for analyzing the resource use of private households that—in future—could
provide a representative basis for a new indicator of consumption in the future using a consumer
panel. The calculator itself could also be developed further as an interactive tool for real-time decision
making in all-day routines and practices.
The aim of this paper is to contribute to the process of examining the consumption patterns of
households and to provide the means for micro-economic SDG indicators. Using the example of
natural resource use, the authors show how environmental indicators can be differentiated for private
household types and categories of consumption. We posit that using microdata is a viable solution to
distinguish between the age, sex, income, ethnicity, geographic location, and other characteristics as
required by national policies in Germany [
28
30
]. We propose to enhance the present highly aggregated
macro-oriented indicator system for SDG 12 with the help of disaggregating microeconomic data
and indicators.
We hypothesize that a micro-based Resource Consumption Indicator could be an adequate tool to
monitor consumer’s Material Footprint and target achievement (measurement but also management).
Differentiating indicators between arrays of consumption and different consumer groups reveal shifts
and changes between arrays and groups that may otherwise stay undetected and camouflaged by
aggregated macro indicators. Additionally, the online tool based Resource Lifestyle Footprint could
help to facilitate achieving the given targets and address the different types of households and lifestyles
appropriately in this process.
We start by introducing the data and methods that we used in Section 2: the Resource
Consumption Indicator (RCI) and the Resource Lifestyle Footprint (RLF). The results are presented in
Section 3, followed by a discussion of limitations and the derived implications in Section 3. In Section 5
(conclusion), we put the results into the context of research and consumer policies.
2. Methodology
The following section describes briefly the methods and data used for calculating the Material
Footprints of both Resource Consumption Indicator and Resource Lifestyle Footprint. The Resource
Consumption Indicator (1) relates top-down resource-intensities of consumption in a country to
expenditures of consumers. The Resource Lifestyle Footprint (2) models the resources of product-services
and their use bottom up. Both approaches account for the amount of extracted abiotic and biotic materials
from nature and relate them annually on a per person or per capita basis.
Sustainability 2018,10, 4467 4 of 16
2.1. The Resource Consumption Indicator (RCI)
The first approach is based on tables on international trade (see Reference [
31
] on multi-regional
footprint analysis). These multi-regional Input-/Output tables (MRIO) allow for the accounting of
globally extracted raw materials (alongside other indicators) for goods consumed within a country
(including imports but excluding exports of an economy). By allocating these goods to the consumption
of households, country-specific resource-intensities are that can be directly linked to country-specific
household expenditures calculated (see also References [
32
,
33
]). This top-down model converts traded
monetary value into the physical material use of households, thus linking the macro-economy with
microdata on the level of households. It provides a holistic view of resource consumption and is
consistent when comparing countries with each other. Thus, it can be used to generate representative
data on the resource use of households in countries and to differentiate levels of resource consumption
depending on socioeconomic characteristics in the microdata. Buhl et al. (2016) [
10
] successfully
applied the resource intensities to household expenditure data for Germany in order to describe the
Material Footprint (sum of globally induced resource extractions) of different households in the federal
state of North-Rhine-Westphalia in Germany itself.
The weakness of this top-down approach is its inability to explain the differences between
household types and their resource consumption sufficiently. The highly aggregated data with respect
to resource intensities based on MRIO tables also does not allow the identification of specific product
and service options for more sustainable measures by households or policies catering towards a more
resource-efficient lifestyle. This is where bottom-up models can help to fill data gaps by focusing on the
most relevant areas of consumption and disaggregating further into different services and products.
To measure private household consumption for the approach of the Resource Consumption
Indicator, data from the German Survey of Household Income and Consumption (EVS) for the years
2003, 2008 and 2013 were used. The EVS is conducted by the Federal Statistical Office, using household
expenditure as a proxy for consumption. The data are structured into eleven main categories and
152 subcategories according to the Classification of Individual Consumption per Purpose (COICOP).
The analysis described here focuses on the eleven main categories. Furthermore, the EVS includes
socioeconomic data and enables a differentiation between different groups of households or individuals,
clustered by characteristics such as age or household net income [10].
Table 1shows the summary statistics for the yearly expenditures of the main categories between
2003 and 2013 in Germany.
Table 1. The overview of selected variables and descriptive statistics of the EVS.
N(Sample Size) Mean Std.Dev. (Standard Deviation)
Variables 2003 2008 2013 2003 2008 2013 2003 2008 2013
Food and beverages 42,744 44,088 42,792 3634.7 3831.69 3825.74 1933.35 2060.8 2100.27
Clothing 42,744 44,088 42,792 1646.34 1514.8 1577.43 1572.12 1569.62 1665.11
Housing 42,744 44,088 42,792 9449.24 9642.25 10,746.5 6337.31 4900.78 5129.06
Furnishing 42,744 44,088 42,792 1900.51 1624.52 1671.71 4191.31 3805.17 3702.8
Health 42,744 44,088 42,792 1332.44 1438.11 1552.15 3822.46 3694.82 4232.48
Transport 42,744 44,088 42,792 4610.35 4687.23 4628.17 11,824.1 10,489.9 11,369.6
Communication 42,744 44,088 42,792 896.51 833.13 821.45 668.83 527.96 554.92
Recreation and Culture 42,744 44,088 42,792 3807.11 3701.09 3575.99 4002.47 4512.23 4743.77
Education 42,744 44,088 42,792 298.38 292.53 272.98 865.56 1046.73 1070.36
Hotels 42,744 44,088 42,792 1477.48 1654.37 1782.77 1890.12 2143.4 2316.07
Miscellaneous 42,744 44,088 42,792 1379.76 1351.69 1297.62 1892.59 2103.99 1948.51
Household size 42,744 44,088 42,792 2.43 2.28 2.10 1.23 1.17 1.09
NRW 9223 7708 7823 1 1 1 0 0 0
Data: German Survey of Household Income and Consumption, 2003, 2008, 2013. Expenditure data in Euro.
“Household size” and “NRW” (i.e., living in the federal state of NRW in Germany) represent socio-demographics of
the sample.
The expenditures of households in the EVS were related to so-called resource intensity factors
(household resource use per Euro). These factors stem from multi-regional input-output analyses
Sustainability 2018,10, 4467 5 of 16
(MRIO) of economy-wide material flow accounts and the continuous household budget surveys for
Germany in the year 2005. Table 2provides an overview of the average resource intensities in the main
consumption categories [34].
Resource intensities allow the measurement of the impact of private consumption on the
environment and can be used to calculate the Material Footprint of consumption. The calculation of
the Material Footprint based on microdata on expenditure (EVS) and respective resource intensities of
the main COICOP categories are described in Appendix A.
The further analysis is based on the differentiation between the main COICOP categories from
“Food and beverages” to “Miscellaneous”. However, Buhl et al. (2016) show an application of the
method that further differentiates within the main COICOP category “Transport” by calculating
resource intensities for specific transport services like local and long distance trains, air travel, or the
use of second-hand cars [10].
Table 2. The resource intensities of private household consumption in Germany.
Consumption Categories Resource Intensity (kg/)
Food and beverages 5.09
Housing etc. 3.18
Furnishings etc. 2.99
Transport 1.50
Restaurants and hotels 1.40
Health 0.60
Education 0.48
Recreation and culture 0.41
Communication 0.37
Clothing 0.19
Miscellaneous 0.19
Based on Buhl et al., 2016 [10]. Data: Buhl and Acosta 2015 [32].
2.2. The Resource Lifestyle Footprint (RLF)
The “Resource Calculator” tool (see https://www.ressourcen-rechner.de/) provides a footprint
of a household’s lifestyles. It calculates the Material Footprint of products, their services, and usage
directly and over the whole lifecycle of their production, use, and end-of-life (including material
extractions in other countries). It combines quantitative (and often physical) survey data on household
consumption with survey data on socioeconomic characteristics and household attitudes in order to
calculate an individual or lifestyle footprint. This approach allows us to identify drivers and barriers of
resource use and matches socio-demographic characteristics, lifestyle decisions, subjective attitudes or
assessments, social norms, and individual preferences as well as budget restrictions to the individual
footprint or ecological backpack (see References [
35
,
36
] on the concept). Resource use can thus be
reduced not only by consuming resource efficient products, but also by improving the service these
products provide. This bottom-up model has been successfully tested in several studies ([
18
,
21
,
26
]) and
is compliant with the Material Flow Accounting (MFA) and Life Cycle Assessment (LCA) methodology.
It is also compatible with generic databases for lifecycle inventories as well as assessments of output
indicators such as carbon footprints (as shown by References [22,23]).
The calculator generates a growing database because of its permanent online accessibility.
Besides questions regarding the most important fields of consumption like housing and mobility,
users can voluntarily and anonymously provide data concerning their socioeconomic characteristics.
This was surveyed alongside other subjective attitudes and norms such as relative household income
in comparison, subjective health or subjective well-being. Table 3lists the different areas of private
consumption in the Resource Calculator.
Sustainability 2018,10, 4467 6 of 16
Table 3. The description of consumption categories in the Resource Calculator.
Groups of Consumption in Resource Calculator Description of Category
Nutrition diets, food waste, and consumption of foodstuffs and drinks
Housing buildings, heat, and electricity use
Consumer Goods appliances, clothes, furniture
Mobility day-to-day travel with cars, bikes, public transport
Leisure hobbies, sports, cultural activities
Vacation vacation travel and accommodation
The Resource Calculator application was advertised via different channels such as the website of
the Wuppertal Institute, online blogs on sustainable living, and reviews of product testing magazines.
Between the launch on 25 February 2015 and 13 February 2017, 49,037 persons participated without any
incentives. Data preparation and the removal of invalid and implausible responses left a database of 44,514
being analyzed. For a more detailed description, necessary transformations and underlying assumptions
see Buhl et al., 2017 [
27
]. Table 4comprehends the most relevant dimensions and variables surveyed by
the Resource Calculator. Socioeconomic, personal, and household characteristics, as well as subjective
assessments and other lifestyle features, complement disaggregated information on the Material Footprint.
Table 4. The overview of variables and descriptive statistics of the Resource Calculator.
Statistic N Mean Std. Dev. Min Max
Personal characteristics
Female 26,103 0.62 0.49 0 1
Age 24,596 36.00 12.00 18 71
Schooling years 26,118 14.00 3.20 9 21
Occupational status 18,463 3.00 1.10 1 4
Unemployed 18,463 0.14 0.35 0 1
Household characteristics
Household size 44,238 2.20 1.00 1.00 6.00
Number of children 9119 1.60 0.71 1 4
Size of dwelling (m2)30,482 95.00 47.00 7.00 300.00
Subjective assessments
Subjective health 17,297 1.30 0.57 1 2
Relative income 22,125 0.41 1.00 2 2
Life satisfaction 26,041 7.30 1.80 1 10
Social ties satisfaction 17,690 1.00 0.71 2 2
Lifestyle
Diet 44,317 2.20 0.84 1 4
Vegetarian 44,317 0.33 0.47 0 1
Hobby hours 44,091 8.00 12.00 0.00 75.00
Days on vacation 44,056 15.00 13.00 0 81
Trips (in km) 44,086 220.00 327.00 0.00 1,800.00
Material Footprints (kg)
Housing 44,068 8722.00 4059.00 45 26,804
Consumer goods 44,068 2859.00 1161.00 2 6936
Nutrition 44,068 5160.00 1323.00 82 9145
Leisure 44,069 446.00 639.00 0 5113
Mobility 43,456 6682.00 6407.00 1 39,447
Vacations 44,068 1525.00 1532.00 0 10,200
Overall Material Footprint 44,068 25,897.00 10,041.00 2.711 76,570
Note: Descriptive statistics include the number of observations (N), mean, standard deviation (Std.Dev.),
minimum (Min.) and maximum (Max.) of observations. “Trips” is the distance in km for trips and events
during the past month. “Days on vacation” are days on vacation overall in the past year. “Hobby hour” are the
hours overall spent on hobbies on average per month. “Social ties satisfaction” is the personal evaluation on how
often social relations are perceived as satisfying (as the Likert scale). “Relative income” is the assessment of the
household net income in comparison (as the Likert scale).
Sustainability 2018,10, 4467 7 of 16
3. Results
The following section shows original results as well as results from recent studies on the Resource
Calculator using the methods and data sets described in Section 2.
3.1. Resource Consumption Indicator Based on Microdata EVS and Resource Intensities
The R
CF
was used to monitor the resource use of private households in the sustainability report
of the Ministry for Environment, Agriculture, Conservation, and Consumer Protection of the State of
North Rhine-Westphalia (NRW). One aim was to examine if and to which extent the Resource Indicator
can contribute to the goals and indicators of SDG 12 and how it could be improved. For this purpose,
the EVS data and the resource intensity data described in Section 2.1. were used as a database.
The Material Footprint of private households in NRW accounted for 31 t per capita in 2013.
Using microdata enabled the researchers to further analyze the distribution of the Material Footprint
among households. Figure 1shows a right-skewed distribution although the 99
th
percentile was
removed. This implies a relatively strong bias of the average Material Footprint due to relatively few
households being responsible for relatively high amounts of resource use.
Sustainability 2018, 10, x FOR PEER REVIEW 7 of 16
The following section shows original results as well as results from recent studies on the
Resource Calculator using the methods and data sets described in Section 2.
3.1. Resource Consumption Indicator Based on Microdata EVS and Resource Intensities
The RCF was used to monitor the resource use of private households in the sustainability report
of the Ministry for Environment, Agriculture, Conservation, and Consumer Protection of the State of
North Rhine-Westphalia (NRW). One aim was to examine if and to which extent the Resource
Indicator can contribute to the goals and indicators of SDG 12 and how it could be improved. For this
purpose, the EVS data and the resource intensity data described in Section 2.1. were used as a
database.
The Material Footprint of private households in NRW accounted for 31 t per capita in 2013. Using
microdata enabled the researchers to further analyze the distribution of the Material Footprint among
households. Figure 1 shows a right-skewed distribution although the 99th percentile was removed.
This implies a relatively strong bias of the average Material Footprint due to relatively few
households being responsible for relatively high amounts of resource use.
Figure 1. The distribution of Material Footprint (years 2003, 2008, 2013) according to Buhl et al., 2016
[10].
The application of the indicator of private household data from NRW revealed three categories
that accounted for the highest shares in resource consumption: housing, food, and transport [32,37].
However, smaller shares on household expenditure do not necessarily lead to lower Material
Footprints, as resource intensities can be very different between categories of consumption.
Figure 2 shows the overall change in resource use of private households in NRW between 2003
and 2013. The environmental impact of these relative changes in resource consumption depends on
the share of the categories in the overall Material Footprint. On the one hand, Communication, for
example, exhibits a strong increase of more than 30%, which might come from rapid innovations in
information and communication technologies. On the other hand, transport, for example, exhibits a
Figure 1.
The distribution of Material Footprint (years 2003, 2008, 2013) according to Buhl et al., 2016 [
10
].
The application of the indicator of private household data from NRW revealed three categories
that accounted for the highest shares in resource consumption: housing, food, and transport [
32
,
37
].
However, smaller shares on household expenditure do not necessarily lead to lower Material Footprints,
as resource intensities can be very different between categories of consumption.
Figure 2shows the overall change in resource use of private households in NRW between 2003
and 2013. The environmental impact of these relative changes in resource consumption depends on the
share of the categories in the overall Material Footprint. On the one hand, Communication, for example,
exhibits a strong increase of more than 30%, which might come from rapid innovations in information
and communication technologies. On the other hand, transport, for example, exhibits a decrease in
Sustainability 2018,10, 4467 8 of 16
the Material Footprint. Buhl et al. (2017) differentiate resource intensities in “Transport” and show
that a decrease in its Material Footprint comes from, e.g., a reduction of gas consumption, reduced car
ownership, and “other” reasons. Due to the relatively high resource intensity of transport, a small
decrease in expenditure for transport cancels out a larger increase in expenditure for communication
services and technologies between 2003 and 2013.
Figure 2.
The change in the Material Footprint in NRW 2003–2013 according to Buhl et al., 2016 [
16
].
Data: Buhl and Acosta 2015 [32].
In sum, the total resource use in NRW remained almost unchanged over the three reporting
periods with a reduction of 3.9% between 2003 and 2013 on a comparable high level of resource use.
It is interesting to note that this small change in total is a result of significant shifts between the different
fields of consumption. This implies that consumption patterns in NRW changed, even though the
overall resource use did not by a large margin [16].
3.2. Resource Lifestyle Footprint Based on Survey Data from an Online Web Tool
The overall Material Footprint of users of the Resource Calculator accounts for 26 t per user
(and year). The distribution of the Material Footprint shows a similar right-skewed distribution as
revealed by the Resource Indicator. This corroborates our findings that the Material Footprint of
private households is strongly biased by high resource use of relatively few households.
Figure 3shows the six categories presented in Table 3and their shares in the respective Material
Footprints of the deciles. It is notable that some shares, such as food and vacation, remain nearly
constant from the first to the tenth decile while others, such as housing and mobility, increase strongly.
This allows us to conclude that the potential main drivers of a high Material Footprint appear to be
these categories.
Sustainability 2018,10, 4467 9 of 16
Sustainability 2018, 10, x FOR PEER REVIEW 9 of 16
Figure 3. The Material Footprints for deciles and category shares.
Users have been asked to classify their income in respect to the average household net income
on a symmetric scale from clearly below average to clearly above average. Surveying the relative
household net income makes it easier for users of the calculator to state their net income and to
prevent non-response of users. Again, the results reveal an increasing Material Footprint with
increasing household net income (see Figure 4).
Figure 4. The relative household net income categories and related mean Material Footprint.
The data was also used by Buhl et al., 2017 [27] to examine the relationship between the Material
Footprint and life satisfaction. As postulated by Buhl et al., 2017, the use of natural resources is not
clearly linked to users subjective well-being (see Figure 5).
Figure 3. The Material Footprints for deciles and category shares.
Users have been asked to classify their income in respect to the average household net income
on a symmetric scale from clearly below average to clearly above average. Surveying the relative
household net income makes it easier for users of the calculator to state their net income and to prevent
non-response of users. Again, the results reveal an increasing Material Footprint with increasing
household net income (see Figure 4).
Sustainability 2018, 10, x FOR PEER REVIEW 9 of 16
Figure 3. The Material Footprints for deciles and category shares.
Users have been asked to classify their income in respect to the average household net income
on a symmetric scale from clearly below average to clearly above average. Surveying the relative
household net income makes it easier for users of the calculator to state their net income and to
prevent non-response of users. Again, the results reveal an increasing Material Footprint with
increasing household net income (see Figure 4).
Figure 4. The relative household net income categories and related mean Material Footprint.
The data was also used by Buhl et al., 2017 [27] to examine the relationship between the Material
Footprint and life satisfaction. As postulated by Buhl et al., 2017, the use of natural resources is not
clearly linked to users subjective well-being (see Figure 5).
Figure 4. The relative household net income categories and related mean Material Footprint.
The data was also used by Buhl et al., 2017 [
27
] to examine the relationship between the Material
Footprint and life satisfaction. As postulated by Buhl et al., 2017, the use of natural resources is not
clearly linked to users subjective well-being (see Figure 5).
Sustainability 2018,10, 4467 10 of 16
Sustainability 2018, 10, x FOR PEER REVIEW 10 of 16
Figure 5. The scatter and line prediction plot of the Material Footprint (in kg) vs. life satisfaction (10-
point scale). Confidence band with 𝑎=0.01. Buhl et al., 2017 [27].
To test their hypothesis, Buhl et al., 2017 conducted a stepwise multivariate regression analysis.
They found that the strongest impacts on life satisfaction are measured for subjective health and for
satisfaction with social ties. Real income and gender reveal smaller, but still notable effects. The
influence of age, vacation days, and the Material Footprint is rather weak. Subjective assessments and
norms appear to have the strongest impact on subjective well-being, followed by socio-demographic
characteristics that seem to have less relevance in this context. The Material Footprint has the smallest
impact with a slightly negative effect on life satisfaction [27].
Data from the Resource Calculator allows for the disaggregation of Material Footprints and the
analysis of complex research questions in the realms of empirical consumer research regarding the
link between socioeconomic features and the Material Footprint. In addition, the online web tool
approach allows for a quick and flexible alteration of the variables surveyed and a constant flow of
survey data.
4. Discussion
The results presented in the previous sections are based on two methods to analyze the Material
Footprint of private households according to the requirements of SDG 12. Both concepts have certain
strengths as well as potential weaknesses or limitations.
4.1. Resource Consumption Indicator
Regarding the RCI, limitations are the relatively high data aggregation of the main categories and
the restriction to consumption expenditure as a proxy for consumption.
Using aggregated data limits the differentiation ability in regard to the consumption of products
and services. For instance, one euro invested in the construction of a private house cannot be
differentiated from another one invested in maintaining heating. Both are equally subsumed under
“housing”. Due to this, the depth of analysis of resource use related to certain consumption patterns
is restricted. Lifecycle data could be used to extend the current database by disaggregating resource
intensities for specific products and services. Such an improved disaggregation of data was
successfully conducted by Buhl et al., 2017 for transport and mobility services [37].
Additionally, expenditure data are used as a proxy for consumption. Expenditure data are
available in internationally harmonized, official and representative household statistics, which
Figure 5.
The scatter and line prediction plot of the Material Footprint (in kg) vs. life satisfaction
(10-point scale). Confidence band with a=0.01. Buhl et al., 2017 [27].
To test their hypothesis, Buhl et al., 2017 conducted a stepwise multivariate regression analysis.
They found that the strongest impacts on life satisfaction are measured for subjective health and
for satisfaction with social ties. Real income and gender reveal smaller, but still notable effects.
The influence of age, vacation days, and the Material Footprint is rather weak. Subjective assessments
and norms appear to have the strongest impact on subjective well-being, followed by socio-demographic
characteristics that seem to have less relevance in this context. The Material Footprint has the smallest
impact with a slightly negative effect on life satisfaction [27].
Data from the Resource Calculator allows for the disaggregation of Material Footprints and the
analysis of complex research questions in the realms of empirical consumer research regarding the link
between socioeconomic features and the Material Footprint. In addition, the online web tool approach
allows for a quick and flexible alteration of the variables surveyed and a constant flow of survey data.
4. Discussion
The results presented in the previous sections are based on two methods to analyze the Material
Footprint of private households according to the requirements of SDG 12. Both concepts have certain
strengths as well as potential weaknesses or limitations.
4.1. Resource Consumption Indicator
Regarding the R
CI
, limitations are the relatively high data aggregation of the main categories and
the restriction to consumption expenditure as a proxy for consumption.
Using aggregated data limits the differentiation ability in regard to the consumption of products
and services. For instance, one euro invested in the construction of a private house cannot be
differentiated from another one invested in maintaining heating. Both are equally subsumed under
“housing”. Due to this, the depth of analysis of resource use related to certain consumption patterns
is restricted. Lifecycle data could be used to extend the current database by disaggregating resource
intensities for specific products and services. Such an improved disaggregation of data was successfully
conducted by Buhl et al., 2017 for transport and mobility services [37].
Additionally, expenditure data are used as a proxy for consumption. Expenditure data are
available in internationally harmonized, official and representative household statistics, which ensure
continuous surveys and high data quality. There is a proven correlation between expenditure,
consumption and hence resource use. However, there are other factors influencing the measurable
Sustainability 2018,10, 4467 11 of 16
impact on the environment. Disregarding those factors can cause bias. Examples of such factors are
the households’ repairing behavior, their willingness to decide on second-hand goods, and the way
and intensity of using a certain good. Value conceptions may also lead a household to opt for goods
that are more expensive than comparable alternatives, but cause a similar resource use [16].
4.2. Resource Lifestyle Footprint
The method and data limitations of the R
LF
stem from non-representative sampling on the one
hand and the necessary time efficiency of the survey on the other hand.
Even though the Resource Calculator provides a large amount of user data due to the high usability
and, thus, the acceptance of the calculator tool, the sample includes some bias due to the voluntary
sampling. The share of young female users, vegetarians, and vegans, for example, is disproportionally
high, which indicates a self-selection of pro-environmental users [
27
]. Future studies should aim for
a more representative sampling when using the Resource Calculator as a survey tool. Adding more
detailed questions regarding personal information about the users themselves could increase the
informative value and the representativeness of the database.
The second limitation of the Resource Calculator relates to the requirement to conduct a survey
within a certain amount of time. This results in a limited set of questions that do not allow us to
analyze every aspect of consumer’s consumption patterns. Many products and services were omitted
from the survey (e.g., compared to the “diary” approach in Reference [
26
]), because they would
not contribute much to a higher footprint. Other questions were simplified, aiming at helping the
households to complete the survey rather than asking for precise physical values. Finally, even the
most comprehensive bottom-up survey would exclude certain products and could not account for
every variation of the product types. So there is always some part of the Material Footprint that cannot
be related to households individually. Further analysis of the available footprint data could help to
identify the essential questions, e.g., by means of unsupervised learning and by using an average
pedestal of resource consumption for areas of a low importance (e.g., durable goods such as jewelry or
the use of non-living space).
5. Conclusions
5.1. Summary
We introduced two methods for analyzing the Material Footprint of private households based
on microdata. The first method (resulting in the Resource Consumption Indicator) is based on
expenditure data according to internationally harmonized COICOP. The second method (resulting in
the Resource Lifestyle Footprint) is based on survey data from a web tool called Resource Calculator.
Both methods allow us to differentiate the Material Footprint along arrays of consumption like
housing and mobility as well as socioeconomic characteristics like age or income and thus meet
the disaggregation requirement to SDG indicators. The results from applying the two methods in
Germany shows that the Material Footprint ranges between 26 t and 31 t per capita in Germany and its
distribution is right-skewed. The most relevant categories are housing, mobility, and nutrition. When it
comes to disaggregating the Material Footprint along socioeconomic characteristics, we showed that
an increasing household net income leads to an increasing Material Footprint.
5.2. Methods
Using microdata from the statistical offices for a Resource Indicator offers three main benefits [
32
].
First, it enables a representative depiction of private household consumption.
Second, private household consumption can be examined by looking at differentiated
consumption categories such as energy or food and its shifts and changes for the past decades.
Sustainability 2018,10, 4467 12 of 16
Third, private household consumption can be examined by looking at differentiated population
groups (disaggregated for example by income or age).
Analyzing consumption by using resource intensities offers a possibility to evaluate its
environmental impact. The concept relies on a representative, internationally harmonized and thus
comparable data according to COICOP that is available in different countries.
The method used for the Resource Lifestyle Footprint (based on the Resource Calculator) provides
new options for consumers to receive real-time feedback and for researchers to collect and gather data
quickly, flexibly, and constantly over time [
27
]. Further research regarding the impact of socioeconomic
characteristics on resource use could help us to identify appropriate reduction strategies for different
groups of consumers as Lettenmeier 2018 successfully showed [
38
]. Moreover, we strive to collect more
data from users abroad in order to compare the Material Footprints internationally (e.g., in a current
project on sustainable lifestyles in 7 different countries). So far, the sample of users from abroad is too
small to conduct a proper comparative analysis.
Despite some weaknesses, the presented Resource Consumption Indicator appears to be a good
and expandable method for measuring the resource use of private households according to SDG 12.
However, an improved database is crucial for increased reliability. This issue could be addressed by
collecting lifecycle data.
The Resource Lifestyle Footprint is a promising attempt but should be improved regarding the
aforementioned limitations. Especially, it will be important to focus on improving the database to
receive a more representative sample while condensing the questions about resource use to the most
essential ones. In this regard, the survey instrument that indicates the Material Footprint of private
households could be incorporated into existing representative surveys like the Socioeconomic Panel
(SOEP) or GESIS Panel in Germany or equivalent panels on a European level, e.g., the European
Community Household Panel (ECHB). This way, environmental policy evaluation and research
on sustainable consumption would benefit from the longitudinal design of the surveys and link
environmental issues with an extensive set of socioeconomic predictors efficiently. In any case, it would
be helpful to gather more detailed personal and household information to facilitate differentiation
between them.
Combining the presented tools could address some of the aforementioned limitations and further
improve the usability of microdata for measuring progress towards achieving SDG 12. The Resource
Consumption Indicator offers a possibility to measure this progress over time and the status quo.
The Resource Lifestyle Footprint can provide a new and more differentiated micro-level database for
analyzing consumption-related resource use. The combination of both methods (or similar methods
with microdata for that matter) would also enhance scenario building. As the majority of environmental
scenarios currently focus on technological and economic feasibility, there is a lack of scenario models
that also investigate the social and cultural drivers and barriers of sustainable development [39].
5.3. Policy Making
The Resource Consumption Indicator and the Resource Lifestyle Footprint appear to be promising
tools for deepening the understanding of private household consumption, the interaction of production
and consumption patterns, and detecting unused potentials to increase its sustainability according
to the SDGs. First results from applying the tools already revealed insights about the structure of
the resource use of private households. We conclude that microeconomic data offers an important
enhancement of the present macro data-based indicator system. Indicators based on microdata are
able to evaluate and inform in a differentiated and disaggregated way, in perspective even in real
time. As such the methods reveal shifts in resource use between different arrays of consumption and
consumer groups that would otherwise stay undetected and camouflaged by highly aggregated macro
indicators. Policy evaluations benefit from a disaggregated perspective on the Material Footprint of
private household instead of evaluating the overall trend in the Material Footprint. Policymakers
may wonder why efforts to reduce the natural resource in mobility does not show a decreasing
Sustainability 2018,10, 4467 13 of 16
overall Material Footprint, e.g., due to indirect rebound effects and shifts of consumption patterns.
For instance, differentiating the Material Footprint along arrays of consumption allows us to evaluate
whether a reduction of the Material Footprint in mobility is offset by an increase in natural resource
use by housing or communications. As such, policymakers may identify which policies in specific
arrays of consumption may be more effective in reducing the Material Footprint since rebound effects
and shifts of consumption are less pronounced.
A more differentiating approach to indicators is not relevant politically (for (inter)national
policies and programs like the indicator framework of SDGs and national programmes striving to
implement them), but also in everyday decision-making situations, whether in the company (products,
infrastructures) or in the household (lifestyles). In fact, the households themselves may evaluate
whether changes in one array of consumption are offset by shifts of their consumption into other
arrays. For instance, private households may reduce their resource use by foregoing resource-intensive
vacations abroad. At the same time, they may become aware that their savings are offset due to
intensified leisure activities.
Indicators based on microdata (or indicator set for different goals) are fundamental for the
implementation of national policies such as the National Program on Sustainable Consumption in
Germany. They allow us to combine efforts for sustainable lifestyles by companies, households and
policymakers alike. Does a product or service contribute to achieving an SDG? Are certain production
and consumption patterns sustainable and to what extent? Which rebounds can be anticipated? Which
trends evolve and do we leave certain groups behind in doing so? Does a policy instrument support
sustainable development or not? This type of evaluation system would—in the long run and combined
with real-time tools—help to manage and measure sustainable development.
Author Contributions:
J.B. and C.L. drafted the thesis. J.B., C.L., J.T. and K.B. conceptualized the paper. J.B. and
J.T. analyzed the data and wrote the paper. K.B. and N.S. contributed research and reviewed the paper. All authors
draw the conclusions.
Funding: This research received no external funding.
Acknowledgments:
No funding was received in support of this paper. The original research and discussion
on the resource use of households was partly funded by the federal Ministry for the Environment in North
Rhine-Westphalia, Germany (Ministerium für Umwelt, Landwirtschaft, Natur-und Verbraucherschutz des Landes
Nordrhein-Westfalen).
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A. Calculating the Material Footprint Based on Resource Intensities
The Resource Indicator is calculated as the Material Footprint of the consumption of private
households. This Material Footprint is the product resulting from the multiplication of the consumption
expenditure c by the resource intensity r.
Material Footprint =c×r (A1)
The consumption expenditure for the jth consumption category of k consumption categories in
total is calculated as the arithmetic mean of the consumption expenditure of the ith household out of n
households in total in time t (measured in years).
cj=1
n
n
i
ct
ij (A2)
The consumption expenditure is then adjusted for inflation to the base year t by considering the
inflation rate
π
of the subsequent years in the respective consumption category j. This prevents a bias
resulting from inflation.
cj=1
n"n
i
ct
ijct+1
ij ×πt+1
j#(A3)
Sustainability 2018,10, 4467 14 of 16
The consumption expenditure of the ith household is put into the context of the household size h
to obtain a per capita result instead of a per household result.
cj=1
n"n
i
[(ct
ij(ct+1
ij ×πt+1
j))/ht
i## (A4)
To enable a differentiation by different subgroups, Buhl et al. introduce a dimension X,
representing socioeconomic characteristics like household income.
cj=1
n"n
i
[(ct
ijX(ct+1
ijX ×πt+1
j))/ht
iX## (A5)
The resource intensity is calculated by dividing the total resource use (indicated by household
consumption) Rt
jby the associated consumption expenditure in category j in year t (Equation (A3)).
rj=Rt
j
n
ict
ij
(A6)
The Material Footprint can be expressed as the arithmetic mean of the respective Material
Footprints of the jth consumption category out of k consumption categories in total by inserting
Equations (A5) and (A6) into Equation (A1):
Material Footprint =1
kx
1
nx
k
jn
i[(ct
ijX(ct+1
ijX ×πt+1
j))/ht
iX]×Rt
j
n
ict
ij
(A7)
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2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
... It is also necessary to monitor various sustainability indicators along the value chains. In target areas 12.2 and 12.3, for example, the consumption indicator NRW (Northrhine Westphalia is one of the German federal states or Bundesländer) already shows a higher possibility of differentiation into different consumption fields and product areas (Buhl et al. 2018), since it is based on the income and consumer sample. Overall, the consumption indicator NRW addresses fundamental target areas of the SDG 12, simply by being able to differentiate between the different consumption and product areas. ...
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... In terms of material and waste footprints, there is no concluding evidence to show that high-income households contribute more than low-income households (Buhl et al., 2018;Cai et al., 2019;Lopez et al., 2017). As Buhl et al. (2018) put it, "smaller shares on household expenditure do not necessarily lead to lower material footprints, as resource intensities can be very different between categories of consumption". ...
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The 2015 Paris Agreement marked a significant shift in public and scholarly discourse on climate change by laying more emphasis on stakeholder involvement and local level policies in achieving sustainability transitions. Sustainable Development Goal 12 further emphasises the importance of households in achieving sustainable consumption before 2030, especially in urban centres. However, since its adoption in 2015, no study has sought to synthesise the scholarship conducted around the emerging urban household consumption challenges that could inhibit the achievement of Goal 12. Through a systematic selection and in-depth review of relevant literature over the last five years, this paper assesses these sustainability challenges and critically examines the policy implications for achieving sustainable consumption. The review notes that in recent years, researchers have explored a range of issues including energy consumption, sustainable lifestyles, consumption footprints and class relations in urban household consumption through models and social perspectives. The urban household challenges identified include, inter alia: intensifying household consumption; rising commodification of household activities; continued reliance on unsustainable energy sources; low levels of sustainability education; high costs of sustainable lifestyles; and class differences in sustainable consumption patterns. In addressing these problems, the literature suggests strategies such as greening urban infrastructure, involving households in intervention programmes and promoting sustainability education, among others. Furthermore, to achieve Goal 12, future research and policy initiatives should consider the impact of materiality in household consumption, explore the interlinkages of household consumption with wider socio-cultural institutions and be more practice-oriented.
... The lower Material Footprint of our analysis may come as a result of sample bias towards users with more pro-environmental behaviour, as indicated by their willingness to pay higher prices for greener products. Furthermore, the sample may over-represent young and female participants in Germany (Buhl et al., 2017b(Buhl et al., , 2018. Note: Descriptive statistics include the number of valid observations (n), the mean, the standard deviation (sd), minimum (min) and maximum (max) number of observations. ...
... First, we present the Material Footprint according to its partial components, from housing to holidaymaking. Buhl et al. (2018) also provide descriptive results of an earlier version of the data with less observations. More importantly Buhl et al. (2018) give no multivariate correlations and no indication of their significance, but rather present a first glance of an earlier version of the data. ...
... Buhl et al. (2018) also provide descriptive results of an earlier version of the data with less observations. More importantly Buhl et al. (2018) give no multivariate correlations and no indication of their significance, but rather present a first glance of an earlier version of the data. We, in contrast, provide multivariate findings providing test statistics of the significance of the correlations. ...
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Footprint calculators are efficient tools to monitor the environmental impact of private consumption. We present the results of an analysis of data entered into an online Material Footprint calculator undertaken to identify the socioeconomic drivers of the Material Footprint in different areas of consumption, from housing to holidaymaking. We developed regression models to reveal (1) the impact of socioeconomic characteristics on Material Footprints of private households and (2) correlations between the components of Material Footprints for different arrays of consumption. Our results show that an increasing Material Footprint in one array of consumption comes with an increasing Material Footprint in all other arrays, with the exception of housing and holidaymaking. The socioeconomic characteristics of users have a significant impact on their Material Footprints. However, this impact varies by the array of consumption. Households only exhibit generally bigger Material Footprints as a result of higher incomes and larger dwellings. We conclude that indicators which strive to monitor resource efficiency should survey disaggregated data in order to classify the resource use to different population groups and arrays of consumption.
... Germany set up a national strategy specifically on natural resource management and resource efficiency in households). The Material Footprint is calculated using life cycle analysis (LCA) linked to the life cycle inventory (LCI) database ecoinvent from 2015 (see also [14][15][16], and, for a more extensive description of the method [17][18][19][20][21][22]). ...
... The Material Footprint calculator also surveys personal and household data of users. The influence of personal and household features like income on the Material Footprint has already been covered elsewhere and is thus not the subject of the following analysis (see [14][15][16]). ...
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Recent research on the natural resource use of private consumption suggests a sustainable Material Footprint of 8 tons per capita by 2050 in industrialised countries. We analyse the Material Footprint in Germany from 2015 to 2020 in order to test whether the Material Footprint decreases accordingly. We studied the Material Footprint of 113,559 users of an online footprint calculator and predicted the Material Footprint by seasonally decomposed autoregressive (STL-ARIMA) and exponential smoothing (STL-ETS) algorithms. We find a relatively stable Material Footprint for private consumption. The overall Material Footprint decreased by 0.4% per year between 2015 and 2020 on average. The predictions do not suggest that the Material Footprint of private consumption follows the reduction path of 3.3% per year that will lead to the sustainable consumption of natural resources.
... A significant variable and indicator of a consumer's behavior towards pro-environmental consumption is the possession or availability of wealth. Numerous studies represent how in households that are considered to have high incomes, have higher instances of consumption, and a negative carbon and material footprint is observed [34][35][36][37][38]. For example, examining household energy use in Qatar showed that there is an elevated consumption of energy due to the fact that Qatar households have high incomes and cheap energy availability [39]. ...
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Sustainable consumption (SC) is the concept surrounding the use of products and services with minimal impact on environmental safeguarding resources for current and future generations. Since its implementation in 2015, SC is an expanding area of research as the increased occurrence of environmental impacts is observed globally. In this article, a literature review of sustainable consumption and education is presented as an in-depth review of relevant literature over the last 25 years. The review provides an understanding of the relationship, effect, and current concepts of sustainability education and consumption behavior. An analysis of the historical, geographical, and thematic characteristics of the relevant literature provided the scholarly context of the literature. An exploration into consumer behaviors on an individual and contextual level is presented, highlighting key factors for achieving sustainable consumption on the consumer level. A further review on the effect of education in general, and higher education on consumer behavior, is provided, noting the key findings for the support of sustainable education, as well as the anticipated barriers. In the conclusion, the effect of education on consumption is found to be positive and significant for pro-environmental consumption behaviors, and it is the main approach for implementing the ideals of sustainable consumption in the future.
... The consumption effects that occur are decentralised because consumer products are used in households and not in a monitored company's supply chain. Here, they distinguish between primary data from specific users and secondary data from research panels (for a comparison of both approaches see Buhl et al., 2018). To gather consumption data, additional competences must be acquired. ...
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Quantitative environmental assessments are crucial in working effectively towards sustainable production and consumption patterns. Over the last decades, life cycle assessments (LCA) have been established as a viable means of measuring the environmental impacts of products along the supply chain. In regard to user and consumption patterns, however, methodological weaknesses have been reported and, several attempts have been made to improve LCA accordingly, for example, by including higher order effects and behavioural science support. In a discussion of such approaches, we show that there has been no explicit attention to the concepts of consumption, often leading to product-centred assessments. We introduce social practice theories in order to make consumption patterns accessible to LCA. Social practices are routinised actions comprising interconnected elements (materials, competences, and meanings), which make them conceivable as one entity (e.g. cooking). Because most social practices include some sort of consumption (materials, energy, air), we were able to develop a framework which links social practices to the life cycle inventory of LCA. The proposed framework provides a new perspective of quantitative environmental assessments by switching the focus from products or users to social practices. Accordingly , we see the opportunity in overcoming the reductionist view that people are just users of products, and instead we see them as practitioners in social practises. This change could enable new methods of interdisciplinary research on consumption, integrating intend-oriented social sciences and impact-oriented assessments. However, the framework requires further revision and, especially, empirical validation.
... Long-term studies on the actual environmental impact of collaborative consumption and voluntary simplicity need to be conducted and recognized by policy makers as well [69]. ...
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Transcending the conventional debate around efficiency in sustainable consumption, anti-consumption patterns leading to decreased levels of material consumption have been gaining importance. Change agents are crucial for the promotion of such patterns, so there may be lessons for governance interventions that can be learnt from the every-day experiences of those who actively implement and promote sustainability in the field of anti-consumption. Eighteen social innovation pioneers, who engage in and diffuse practices of voluntary simplicity and collaborative consumption as sustainable options of anti-consumption share their knowledge and personal insights in expert interviews for this research. Our qualitative content analysis reveals drivers, barriers, and governance strategies to strengthen anti-consumption patterns, which are negotiated between the market, the state, and civil society. Recommendations derived from the interviews concern entrepreneurship, municipal infrastructures in support of local grassroots projects, regulative policy measures, more positive communication to strengthen the visibility of initiatives and emphasize individual benefits, establishing a sense of community, anti-consumer activism, and education. We argue for complementary action between top-down strategies, bottom-up initiatives, corporate activities, and consumer behavior. The results are valuable to researchers, activists, marketers, and policymakers who seek to enhance their understanding of materially reduced consumption patterns based on the real-life experiences of active pioneers in the field.
... For a descriptive and bivariate analysis of an earlier version of the data see Buhl, Liedtke, Teubler, Bienge, and Schmidt (2018). They show for instance that higher income implies higher Material Footprints, but higher Material Footprints do not imply higher subjective well-being (the latter more detailed in Buhl et al. 2017b). ...
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Green Information Systems in general, and footprint calculators in particular, are promising feedback tools to assist people in adopting sustainable behaviour. Therefore, a Material Footprint model for use in an online footprint calculator was developed by identifying the most important predictors of the Material Footprint of the calculator’s users. By means of statistical learning, the analysis revealed that 22 of the 95 predictors identified accounted for 74% of the variance in Material Footprints. Ten predictors out of the 95, mainly from the mobility domain, were capable of showing a prediction accuracy of 61%. The authors conclude that 22 predictors from the areas of mobility, housing and nutrition, as well as sociodemographic information, accurately predict a person’s Material Footprint. The short and concise Material Footprint model may help developers and researchers to enhance their information systems with additional items, while ensuring the data quality of such applications.
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New evidence is provided on the determinants of the carbon footprint (CF) at the household level, using the Spanish case as an example and data from the Household Budget Survey (HBS) and the E-MRIO database. The research presents two new contributions. On the one hand, the basis of analysis on what we call ‘Excess per capita CF’, that is, the part of CF that exceeds a threshold associated with a minimum per capita consumption of each product in a household, below which level it is difficult to expect reductions in consumption. Second, the use of a quantile regression (QR) approach for the estimation of the drivers of CF. Both issues imply important changes in the consideration of the influence of some drivers considered so far in the literature, related to which CF quantile the household is in. These differences between an ordinary least squares (OLS) and the QR are especially significant for variables such as income, household size, occupation, age, household composition, housing area and area of residence.
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Spangenberg, Joachim H. 2019. Special Issue Editorial. The global ecosphere is a complex, evolving system, and the anthroposphere another, more rapidly evolving one. Globalization and telecoupling are enhancing their complexity, and even more that of the coupled socio-ecological system [1]. Sustainable development as a global normative development concept and as defined by the 2030 Agenda and its Sustainable Development Goals (SDGs) adds another level of complexity [2]. As a result, the demand for tools to identify transformative innovations, assess future risks, and support precautionary decision-making for sustainability is growing by the day in business and politics. Scenarios are a means of simplification, reducing real-world complexity to a potentially high but limited number of factors, analyzing their interactions, and supporting policy formulation [3]. However, they are not “objective” representations of reality but to a certain degree cannot but reflect orientations and norms held by their authors [4]. While political or management demands can emerge rather spontaneously, scenario development takes time—the demand for climate scenarios with a maximum 1.5 °C of global warming took the Intergovernmental Panel on Climate Change IPCC by surprise and required almost three years to be fulfilled. Integrated models are at the core of the IPCC 1.5° report, but also used all over the world for sustainable development assessment and strategy development. Nevertheless, they (and in particular the economic computable global equilibrium (CGE) models most of them incorporate) are criticized for a lack of transparency, implicit normative assumptions, technical insufficiencies, political bias and an inability to capture the stark and structural changes of the effect-driving mechanisms, in particular the roles of uncertainty and of non-linearities (tipping points). These and other shortcomings limit their reliability as basis for policy development—for instance, the IPCC’s model-based warnings have become more severe with every new report. Is this only due to newly discovered facts, or can one of the reasons be the implicit habit of scientists to avoid type 2 errors (claiming a relationship when it does not exist) at the expense of making type 1 errors (not confirming the existence of a relationship when it exists)? [5] What roles do other habits and routines, and the worldviews of scholars, play in the assumptions made and the interpretations given, in particular in the CGE components? At least the latest IPCC scenarios, assuming ongoing economic growth in affluent countries at the cost of a greenhouse gas overshoot, indicate that scholarly beliefs can trump physical necessities—the economists involved refused to test any scenario analyzing how a no-growth, steady state, or even degrowth economy would work out for social structures, economic prospects, and community flourishing [6]. This is no coincidence but in line with usual procedures of standard economics: so far, the only models used to inform policy choices are at the “optimistic” end of the scale, and within them, functions and parameter choices are taken so “extreme” conclusions are avoided, such as immediately stopping all GHG emissions being the economically optimal policy [7] as this reflects the willingness to pay to avoid future damage [8]. From an environmental point of view, the biophysical perspective must be the basis of scenario development, with social and economic impacts the dependent variables to be managed politically. In particular, “the social” as one of the core dimensions of sustainable development includes the effects and dynamics of public orientations including values and preferences, decision-making mechanisms including equity, gender issues, power statures and democracy, and implementing organizations, their roles, and functions [9]—all factors which often lend themselves better to qualitative description (at best ordinal scale measurement as used by the IPCC and its biodiversity pendant, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services IPBES) rather than quantification [10,11]. Similar questions apply to indicators. They are not only major tools for communicating scenario results but also underpin monitoring selected real-world trends recognized as worth doing so, support communication about monitoring and modelling results and ultimately, aid decision making. However, which parameters are chosen for monitoring and which yardsticks (indicator methods) are applied in doing so often has more to do with established measurement methods, data availability (in particular time series), and data consistency than with which problems are currently of highest importance, politically, ecologically, socially, or economically. If newly emerging trends are not subject to reporting due to missing data time series, this implies that no problem recognized as such less than a quarter century ago can make it into the reporting mechanisms. Similarly, if the data collection focuses on parameters that have been considered relevant under past theoretical assumptions, there is a risk that if such theories are falsified or recognized as outdated, structurally unchanged indicator reports will point in a wrong direction. As a purposive sustainability transition requires environmental innovation and innovation policy, in the first paper in this Special Issue, Daniel Hausknost and Willi Haas discuss the potential and limitations of three dominant strands of literature, the multi-level perspective on socio-technical transitions (MLP), the innovation systems approach (IS), and the long-wave theory of techno-economic paradigm shifts (LWT). They show that in all three approaches, normative societal goals like sustainability are sidelined, and neither strong directionality nor incumbent regime destabilization are societally steered. To overcome these obstacles to the dominant transition theories, they call for new political institutions to make normatively guided selections. Such institutions for transformative innovation are needed to improve the capacities of complex societies to make binding decisions in politically contested fields (Contribution 1). Obviously, for all past successes, scenario development, model building, and deriving indicators deserve and require a permanent critical assessment and in particular, a critical self-reflection of scholars, if they are to maintain and enhance their usefulness in supporting better decision-making in an increasingly complex world. Joachim H. Spangenberg illustrates the deep embeddedness of scenarios and their results in underlying, but not always explicit, theories and how such assumptions shape results and recommendations (Contribution 2). As the scenarios he analyzes are quite archetypical (they come from a large, EU-funded biodiversity research project called ALARM), the lessons-to-be-learnt and the call for reflection on basic assumptions apply to sustainability scenarios more generally. While in the economic scenarios he used, physical parameters like material flows could only be represented indirectly, the more recent ground-breaking work by Giampietro and his team permit the direct integration of energy and emissions in physical terms with labor and economic parameters. Louisa Jane Di Felice, Maddalena Ripa and Mario Giampietro show that by not accounting for the emissions associated with building and maintaining new infrastructure (funds), and particularly those required to increase grid flexibility, the scenarios currently used to inform decarbonization narratives in the EU are missing a key part of the picture (from a biophysical perspective, decarbonization is not just a matter of replacing carbon-intensive with carbon-neutral electricity flows but is also a matter of funds, which in turn are associated with GHG emissions) (Contribution 3). Their analysis suggests that scenarios informing decarbonization policies in the EU are overly optimistic and may lead to an underestimation of the urgency of reducing overall energy consumption to stay within safe carbon budgets. In the following paper of this Special Issue, Anke Schaffartzik and Marina Fischer–Kowalski widen the perspective to the international level, where the transition from traditional renewable to “modern” fossil energy carriers is still the dominating trend—which, due to globally limited supplies and sinks, is not an indefinite option. What affluent and transitioning countries alike need is a sustainability transformation that would change far more than patterns of energy supply and use, rather than only a “Big Push” for renewable energy within pockets of the fossil energy system. They argue that where this far-reaching change requires pushing back against the fossil energy system, the energy underdogs—the latecomers to the fossil energy transition—just might come out on top (Contribution 4). Turning from scenarios to indicators in the second part of the Special Issue, virtually the same challenges continue to haunt scholars and worry the invited authors. Indicators simplify even more than models, but they can make communication much easier. The question then is how to strike a balance, ensuring that the information lost by indicator design makes the result easy to communicate but not potentially misguiding. In particular, in the age of “fake news” and “alternative truths”, simplifications have become suspicious. How can scholars guarantee and demonstrate that their indicators are not misguiding, intentionally or unintentionally, but are reliable and trustworthy scientific results? This is the starting point of Simon Bell and Stephen Morse as they discuss past indicator developments, giving a critical overview of the field as it is today plus a future outlook (Contribution 5). The latter step, rethinking the current state of sustainability indicators and building visions that could reshape the indicator reality is the starting point of Tomás B. Ramos. Based on a critical analysis of a set of challenges and opportunities, he discusses what could be some of the new frontiers and paradigms in sustainability indicators, focusing on three main criteria of valuation: relevancy, feasibility, and societal impacts (Contribution 6). While Ramos promotes new ways of thinking and doing, responding to new global and local paradigms and using transdisciplinary innovations, Rainer Schliep, Ulrich Walz, Ulrich Sukopp, and Stefan Heiland have a different problem to deal with: on invitation by the German Ministry for the Environment and the Federal Agency for Nature Conservation, they were developing new indicators for policy advice and were confronted with two unsatisfactory options. First, a data-driven, bottom-up approach determines indicators primarily by the availability of suitable data. Second, indicators can be developed by a top-down approach, on the basis of political fields of action and related normative goals. While the bottom-up approach might not meet the needs of up-to-date policy advice, the top-down approach might lack the necessary empirical underpinning. For their project, they developed a combined approach that can be considered successful, despite some remaining gaps (Contribution 7). In conclusion, the scientific accuracy of the indicators, the availability of data, and the purpose of policy advice have to be well-balanced when developing indicator systems. Such questions have been, implicitly and explicitly, a matter of dispute in the process of developing and agreeing on indicators to monitor SDG implementation, and a critical examination of reporting so far (several such major global assessments have already appeared) can be considered an appropriate means to identify the likely significant room for improvement. Svatava Janoušková, Tomáš Hák, and Bedřich Moldan highlight that while the current structure of the SDGs has provided a rather firm policy framework, the goals and targets have mostly been operationalized by indicators (Contribution 8). They demonstrate that without a procedurally well-designed conceptual framework for selecting and/or designing indicators, the results of SDG assessments may be ambiguous and confusing. The current SDG indicators tend to fall short of this condition. Johannes Buhl, Christa Liedtke, Jens Teubler, Katrin Bienge, and Nicholas Schmidt also start with the SDGs, and in particular with Goal 12 on sustainable production and consumption, but zoom down to the household level. One indicator named in the SDG for resource use is the material footprint, but a method and disaggregated data basis differentiating the material footprint for production and consumption according to, e.g., sectors, fields of consumption, as well as socioeconomic criteria, does not yet exist. In the search for a solution, they present two microdata-based methods and their results, namely an indicator based on representative expenditure data in Germany and an indicator based on survey data from a web tool (Contribution 9). Indicators based on microdata are particularly useful as they make sure that indicators can be disaggregated by socioeconomic characteristics like age, sex, income, or geographic location. Like the previous authors, Patrizia Modica, Alessandro Capocchi, Ilaria Foroni, and Mariangela Zenga faced data collection problems when assessing the impacts of the European Tourism Indicators System (ETIS) during the period 2013–2016 in a case study in South Sardinia (Contribution 10). With insufficient stakeholder involvement in the implementation process, they find that the objectives of promoting economic prosperity, social equity, cohesion, and environmental protection require an adaptive management approach, including adapting these standardized indicators to different territorial contexts. In particular, the pioneering sustainable tourism performance measurement system (STPMS) can be adapted to meet local needs. Their experience illustrates another challenge to indicator systems: to allow for comparisons, they should be standardized as much as possible, but to be locally meaningful and gain the attention of citizens and decision-makers, they must be adapted to local circumstances. Concepts with standardized categories at higher levels of abstraction, to be filled with context-specific and problem/concern-driven local indicators, have been suggested but so far have not been realized [12], not least because decision-makers appear to have a strong preference for quantitative over semi-quantitative or qualitative indicators—a fallacy of misplaced precision. List of Contributions Hausknost, D.; Haas, W. The Politics of Selection: Towards a Transformative Model of Environmental Innovation. Spangenberg, J.H. Behind the Scenarios: World View, Ideologies, Philosophies. An Analysis of Hidden Determinants and Acceptance Obstacles Illustrated by the ALARM Scenarios. di Felice, L.J.; Ripa, M.; Giampietro, M. Deep Decarbonisation from a Biophysical Perspective: GHG Emissions of a Renewable Electricity Transformation in the EU. Schaffartzik, A.; Fischer-Kowalski, Marina. Latecomers to the Fossil Energy Transition, Frontrunners for Change? The Relevance of the Energy ‘Underdogs’ for Sustainability Transformations. Bell, S.; Morse, S. Sustainability Indicators Past and Present: What Next? Ramos, T.B. Sustainability Assessment: Exploring the Frontiers and Paradigms of Indicator Approaches. Schliep, R.; Walz, U.; Sukopp, U.; Heiland, S. Indicators on the Impacts of Climate Change on Biodiversity in Germany—Data Driven or Meeting Political Needs? Janoušková, S.; Hák, T.; Moldan, B. Global SDGs Assessments: Helping or Confusing Indicators? Buhl, J.; Liedtke, C.; Teubler, J.; Bienge, K.; Schmidt, N.. Measure or Management?—Resource Use Indicators for Policymakers Based on Microdata by Households. Modica, P.; Capocchi, A.; Foroni, I.; Zenga, M. An Assessment of the Implementation of the European Tourism Indicator System for Sustainable Destinations in Italy. Conflicts of Interest The authors declare no conflict of interest. References Haberl, H.; Fischer-Kowalski, M.; Krausmann, F.; Martinez-Alier, J.; Winiwarter, V. A Socio-metabolic Transition towards Sustainability? Challenges for another Great Transition. Sustain. Dev. 2011, 19, 1–14. [Google Scholar] [CrossRef] United Nations General Assembly. Transforming Our World: the 2030 Agenda for Sustainable Development; Resolution 70/1, Document A/RES/70/1, 17th session agenda items 15 and 116, adopted by the General Assembly on September 25th; United Nations: New York, NY, USA, 2015. [Google Scholar] Alcamo, J. 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The long-term transition towards a low-carbon transport sector is a key strategy in Europe. This includes the replacement of fossil fuels, modal shifts towards public transport as well as higher energy efficiency in the transport sector overall. While these energy savings are likely to reduce the direct greenhouse gas emissions of transport, they also require the production of new and different vehicles. This study analyses in detail whether final energy savings in the transport sector also induce savings for material resources from nature if the production of future vehicles is considered. The results for 28 member states in 2030 indicate that energy efficiency in the transport sector leads to lower carbon emissions as well as resource use savings. However, energy-efficient transport sectors can have a significant impact on the demand for metals in Europe. An additional annual demand for 28.4 Mt of metal ores was calculated from the personal transport sector in 2030 alone. The additional metal ores from semiprecious metals (e.g., copper) amount to 12.0 Mt, from precious metals (e.g., gold) to 9.1 Mt and from other metals (e.g., lithium) to 11.7 Mt, with small savings for ferrous metal ores (−4.6 Mt).
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Background Global targets for reducing resource use have been set by organizations such as the International Resource Panel and the European Commission. However, these targets exist only at the macro level, e.g., for individual countries. When conducting an environmental analysis at the micro level, resource use is often neglected as an indicator. No sum parameter indicating all abiotic and biotic raw materials has been considered for life cycle assessment, as yet. In fact, life cycle assessment databases even lack some of the specific input flows required to calculate all abiotic and biotic raw materials. In contrast, the cumulative energy demand, an input-based indicator assessing the use of energy resources, is commonly used, particularly when analyzing energy-intensive product systems. Methods In view of this, we analyze the environmental relevance of the sum parameter abiotic and biotic raw material demand, which we call the material footprint. First, we show how abiotic and biotic raw material demand can be implemented in the Ecoinvent life cycle assessment database. Employing the adapted database, the material footprint is calculated for 12 individual datasets of chosen materials and crops. The results are compared to those of the cumulated energy demand and four selected impact categories: climate change, ozone depletion, acidification, and terrestrial eutrophication. ResultsThe material footprint is generally high in the case of extracted metals and other materials where extraction is associated with a large amount of overburden. This fact can lead to different conclusions being drawn compared to common impact categories or the cumulative energy demand. However, the results show that both the range between the impacts of the different materials and the trends can be similar. Conclusions The material footprint is very easy to apply and calculate. It can be implemented in life cycle assessment databases with a few adaptions. Furthermore, an initial comparison with common impact indicators suggests that the material footprint can be used as an input-based indicator to evaluate the environmental burden, without the uncertainty associated with the assessment of emission-based impacts.
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